view
View regression tree
Syntax
view(tree)
view(tree,Name,Value)
Description
view( returns
a text description of tree)tree, a decision tree.
view( describes tree,Name,Value)tree with
additional options specified by one or more Name,Value pair
arguments.
Input Arguments
|
A regression tree or compact regression tree created by |
Name-Value Pair Arguments
Specify optional
comma-separated pairs of Name,Value arguments. Name is
the argument name and Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN.
|
Display of Default: |
Examples
View Trained Regression Tree
View textual and graphical displays of a trained regression tree.
Load the carsmall data set. Consider a model that explains a car's fuel economy (MPG) using its weight (Weight) and number of cylinders (Cylinders).
load carsmall
X = [Weight Cylinders];
Y = MPG;Train a regression tree using all measurements.
Mdl = fitrtree(X,Y);
View textual display of the trained regression tree.
view(Mdl)
Decision tree for regression 1 if x1<3085.5 then node 2 elseif x1>=3085.5 then node 3 else 23.7181 2 if x1<2371 then node 4 elseif x1>=2371 then node 5 else 28.7931 3 if x2<7 then node 6 elseif x2>=7 then node 7 else 15.5417 4 if x1<2162 then node 8 elseif x1>=2162 then node 9 else 32.0741 5 if x2<5 then node 10 elseif x2>=5 then node 11 else 25.9355 6 fit = 19.2778 7 if x1<4381 then node 12 elseif x1>=4381 then node 13 else 14.2963 8 if x1<1951 then node 14 elseif x1>=1951 then node 15 else 33.3056 9 fit = 29.6111 10 if x1<2827.5 then node 16 elseif x1>=2827.5 then node 17 else 27.2143 11 if x1<3013.5 then node 18 elseif x1>=3013.5 then node 19 else 23.25 12 if x1<3533.5 then node 20 elseif x1>=3533.5 then node 21 else 14.8696 13 fit = 11 14 fit = 29.375 15 if x1<2142.5 then node 22 elseif x1>=2142.5 then node 23 else 34.4286 16 if x1<2385 then node 24 elseif x1>=2385 then node 25 else 27.6389 17 fit = 24.6667 18 fit = 21.5 19 fit = 30.25 20 fit = 16.6 21 if x1<4378 then node 26 elseif x1>=4378 then node 27 else 14.3889 22 if x1<2080 then node 28 elseif x1>=2080 then node 29 else 34.8333 23 fit = 32 24 fit = 24.5 25 if x1<2412.5 then node 30 elseif x1>=2412.5 then node 31 else 28.0313 26 if x1<4365 then node 32 elseif x1>=4365 then node 33 else 14.2647 27 fit = 16.5 28 fit = 34.125 29 fit = 36.25 30 fit = 34 31 if x1<2447 then node 34 elseif x1>=2447 then node 35 else 27.6333 32 if x1<4122.5 then node 36 elseif x1>=4122.5 then node 37 else 14.5313 33 fit = 10 34 fit = 24 35 if x1<2573.5 then node 38 elseif x1>=2573.5 then node 39 else 27.8929 36 if x1<3860 then node 40 elseif x1>=3860 then node 41 else 14.15 37 fit = 15.1667 38 fit = 27.125 39 if x1<2580 then node 42 elseif x1>=2580 then node 43 else 28.2 40 fit = 14.5 41 fit = 13.625 42 fit = 31 43 fit = 27.8889
View graphical display of the trained regression tree.
view(Mdl,'Mode','graph');
View Tree from Bag of Trees
Load the carsmall data set. Consider a model that explains a car's fuel economy (MPG) using its weight (Weight) and number of cylinders (Cylinders).
load carsmall
X = [Weight Cylinders];
Y = MPG;Grow a bag of 100 regression trees using all measurements.
rng(1) % For reproducibility
Mdl = TreeBagger(100,X,Y);Alternatively, you can use fitrensemble to grow a bag of regression trees.
Mdl is a TreeBagger model object. Mdl.Trees stores the bag of 100 trained regression trees in a 100-by-1 cell array. That is, each cell in Mdl.Trees contains a CompactRegressionTree model object.
View a graph of the 10th regression tree in the bag.
Tree10 = Mdl.Trees{10};
view(Tree10,'Mode','graph');
By default, the software grows deep trees for bags of trees.
View Tree from Boosted Ensemble
Load the carsmall data set. Consider a model that explains a car's fuel economy (MPG) using its weight (Weight) and number of cylinders (Cylinders).
load carsmall
X = [Weight Cylinders];
Y = MPG;Boost an ensemble of 100 regression trees using all measurements.
Mdl = fitrensemble(X,Y,'Method','LSBoost');
Mdl is a RegressionEnsemble model object. Mdl.Trained stores the ensemble of 100 trained regression trees in a 100-by-1 cell array. That is, each cell in Mdl.Trained contains a CompactRegressionTree model object.
View a graph of the 10th regression tree in the ensemble.
Tree10 = Mdl.Trained{10};
view(Tree10,'Mode','graph');
By default, fitrensemble grows shallow trees for boosted ensembles of trees. That is, 'Learners' is templateTree('MaxNumSplits',10).
Tips
To view tree t from an ensemble of trees, enter one of these lines
of code
view(Ens.Trained{t})
view(Bag.Trees{t})Ensis a full ensemble returned byfitrensembleor a compact ensemble returned bycompact.Bagis a full bag of trees returned byTreeBaggeror a compact bag of trees returned bycompact.
To save tree in the Command Window, get a
figure handle by using the findall and setdiff functions, and then save tree using the function
saveas.
before = findall(groot,'Type','figure'); % Find all figures view(Mdl,'Mode','graph') after = findall(groot,'Type','figure'); h = setdiff(after,before); % Get the figure handle of the tree viewer saveas(h,'a.png')